View
368
Download
0
Embed Size (px)
Citation preview
1
for the design of sustainable and applicable cropping systems
Meylan, L., Sibelet, N., Gary, C., Rapidel, B.
Combining numerical modeling with a participative approach
Scientific context
• Objective: designing agroforestry systems with multiple performance requirements
• Challenge: interaction of several spp; complex and long-term effects to take into account
• Two common approaches – Modeling: useful for complex interactions often found in
agroforestry; many trials, low cost; but theoretical results and few developed modelsVereijken, 1997; Rapidel et al, 2009; Whitbread et al, 2009
– Participative: involving farmers, suitable solutions, but solutions not genericTixier et al, 2006; Malézieux et al, 2009
?
Case study: shaded coffee in Llano Bonito, Costa Rica• Coffea arabica (mostly Caturra) + shade trees (mostly
Erythrina and Musa spp)• Relatively small farms (1-2ha) supporting many families;
600 farmers in the Llano Bonito watershed (18km²)• Intensive production (400kg of N/ha/yr) sold at
premium price
Case study: shaded coffee in Llano Bonito, Costa Rica• High erosion rates due to steep
slopes and rainy season (+3000 mm of rain/year)
• Operation of downstream hydroelectric dam threatened by sedimentation of Pirris river
• Interest in PES (Payment for Environmental Service) scheme for coffee growers to reduce erosion
Case study: shaded coffee in Llano Bonito, Costa Rica
Coffee production shade trees erosion• Trees help prevent erosion • … but how many trees in coffee systems to
maximize performance in both areas?
Minimum shade level needed – what is the optimum?
?Actual shade tree densities vary between 100 and 400 trees/ha; most trees are pruned 2-3 times a year
Shade trees can have positive or negative effects on coffee production depending on climate, environment, management…
CAF2007: numerical model for coffee shaded systems
Van Oijen et al, 2010
CAF: numerical model for coffee shaded systems
Van Oijen et al, 2010
Can we combine the information from a numerical model with farmers’ knowledge to design sustainable AND practicable systems?
Research objectives
1. Involve coffee growers in the modeling process in order to identify potential systems that farmers would be willing put into place themselves
2. Evaluate usefulness of CAF2007 in generating improved agroforestry systems that are acceptable to farmers
Participative modeling sessions
• Preparation– Previous data and conceptual
model to introduce trees, coffee & erosion relationship
– Progressively introduce new concepts – graphs/charts, model
• Participants asked to think about what they would test “on the field”
• All questions and suggestions recorded verbatim– number of variables in each “what if” question– topics covered
Participative modeling sessions
• 19 farmers in 4 groups; 5 sessions of 2hrs each
S1 •Ideas for experimentation – no model
S2 •Conceptual model, check outcomes of S1
S3 •Numerical data •What does “model” mean?
S4 •Presentation of CAF2007, potential scenarios to be simulated
S5 •Evaluation of simulation outcomes
Participative modeling sessions
Increased participation, complexity of questions, and number of topics covered after introduction of CAF2007 in S4
0
1
2
3
4
5
6
S1 S2 S4 S5
Complexity (mean number of variables per
question)
Working session
Low intensive
Work intensive
Shaded systems
Inputs intensive
0
5
10
15
S1 S2 S4 S5
Diversity (mean number of variables per
farmer)
Working session
Low intensive
Work intensive
Shaded systems
Inputs intensive
Evaluation of CAF2007 by participants
• High interest in simulation of N cycling and fertilization
• Model inaccuracies: effect of shade tree on yield over several years
Mostly agreed with model output
• Fertilization• Mineral N pool• Coffee LAI and
vegetative growth
Mostly disagreed with model output• Shade tree density• Shade tree pruning• Coffee pruning
Missing• Pests & disease• Annual yield
variations (e.g. dieback effect)
Conclusions
• Coffee growers actively engaged with a numerical model to discuss changes in their management– “what if?” and simulation requests; increased
number, scope and complexity with CAF2007– both positive and negative feedback on model
performance– exploration of normally “invisible” processes shown in
model, e.g. erosion, mineral N pool• New systems (change in management) were
identified – to be tested by growers themselves
Contact info:[email protected]@cirad.fr
For more information…pcp-agroforestry.org
ADDITIONAL SLIDES
Typology of coffee plots1: low intensity 2: labor
intensive3: shaded systems
4: agrochemical intensive
Fertilizer (USD/ha/yr) 518 c 1395 a 909 b 1157 a,bFungicide (USD/ha/yr) 51 a,b 28 b 16 b 79 aManual weed control (hrs/ha/yr)
131 a 203 a 120 a 144 a
Shade tree density (#/ha) 288 a,b 332 a,b 539 a 235 b
Yield (t/ha/yr) 4.2 b 8.9 a 7.2 a 8.1 a
Erosion control 1.1 a,b 1.9 a 0.6 b 1.2 a,b
Effect of shade tree density on coffee yield
R² = 0.301
R² = 0.565
2
4
6
8
10
12
0 200 400 600 800 1000
Coffe
e yi
eld
(t/h
a/yr
)
Shade tree density
Group 1
Group 2
Group 3
Group 4
Conceptual model
Conceptual model
Effect of shade trees on coffee
01122334455
full sun Erythrina banana
kg o
f litt
er p
er m
²
shade treatment
0
1000
2000
3000
4000
5000
6000
0.40 0.50 0.60 0.70 0.80 0.90 1.00 1.10 1.20
g of d
ry v
eget
ation
/m²
Infiltration delay (h)
Full sun
Erythrina
Banana
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Full sun Erythrina Banana
Infil
trati
on d
elay
0-3
0cm
(h)
No significant effect of shade on production; but positive effect on soil litter and infiltration, used here as proxies for erosion (actual runoff and erosion data forthcoming)
Preparing the modelVariable Site 1 Site 2 Site 3
YieldInitial simulation 2.36 1.95 3.47
After calibration 1.08 1.22 1.68Coffee LAI
Initial simulation 1.69 2.01 1.2
After calibration 0.52 0.88 0.67Tree LAI
Initial simulation 1.22 1.48 1.63
After calibration 0.89 0.70 0.45Coffee wood biomass
Initial simulation 2.30 2.01 1.87
After calibration 0.34 0.46 0.33Soil water content
Initial simulation 0.22 - 0.14
After calibration 0.06 - 0.04
Left: RMSE for output values used in model calibration
Right: example of calibration effect on
simulated coffee wood biomass values
4000 4100 4200 4300 4400 4500 4600 4700 48000
0.10.20.30.40.50.60.70.80.9
Carbon in coffee wood
after cali-bration
measured data
before cal-ibration
days of simulation
kg o
f C /
m2